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Running
on
Zero
File size: 2,478 Bytes
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import math
import numpy as np
def compute_extrinsic_matrix(elevation, azimuth, camera_distance):
# 将角度转换为弧度
elevation_rad = np.radians(elevation)
azimuth_rad = np.radians(azimuth)
R = np.array([
[np.cos(azimuth_rad), 0, -np.sin(azimuth_rad)],
[0, 1, 0],
[np.sin(azimuth_rad), 0, np.cos(azimuth_rad)],
], dtype=np.float32)
R = R @ np.array([
[1, 0, 0],
[0, np.cos(elevation_rad), -np.sin(elevation_rad)],
[0, np.sin(elevation_rad), np.cos(elevation_rad)]
], dtype=np.float32)
# 构建平移矩阵 T (3x1)
T = np.array([[camera_distance], [0], [0]], dtype=np.float32)
T = R @ T
# 组合成 4x4 的变换矩阵
extrinsic_matrix = np.vstack((np.hstack((R, T)), np.array([[0, 0, 0, 1]], dtype=np.float32)))
return extrinsic_matrix
def transform_camera_pose(im_pose, ori_pose, new_pose):
T = new_pose @ ori_pose.T
transformed_poses = []
for pose in im_pose:
transformed_pose = T @ pose
transformed_poses.append(transformed_pose)
return transformed_poses
def compute_fov(intrinsic_matrix):
# 获取内参矩阵中的焦距值
fx = intrinsic_matrix[0, 0]
fy = intrinsic_matrix[1, 1]
h, w = intrinsic_matrix[0,2]*2, intrinsic_matrix[1,2]*2
# 计算水平和垂直方向的FOV值
fov_x = 2 * math.atan(w / (2 * fx)) * 180 / math.pi
fov_y = 2 * math.atan(h / (2 * fy)) * 180 / math.pi
return fov_x, fov_y
def rotation_matrix_to_quaternion(rotation_matrix):
rot = Rotation.from_matrix(rotation_matrix)
quaternion = rot.as_quat()
return quaternion
def quaternion_to_rotation_matrix(quaternion):
rot = Rotation.from_quat(quaternion)
rotation_matrix = rot.as_matrix()
return rotation_matrix
def remap_points(img_size, match, size=512):
H, W, _ = img_size
S = max(W, H)
new_W = int(round(W * size / S))
new_H = int(round(H * size / S))
cx, cy = new_W // 2, new_H // 2
# 计算变换后的图像中心点坐标
halfw, halfh = ((2 * cx) // 16) * 8, ((2 * cy) // 16) * 8
dw, dh = cx - halfw, cy - halfh
# 初始化一个新的数组来存储映射回原始图像的点坐标
new_match = np.zeros_like(match)
# 将变换后的点坐标映射回原始图像
new_match[:, 0] = (match[:, 0] + dw) / new_W * W
new_match[:, 1] = (match[:, 1] + dh) / new_H * H
#print(dw,new_W,W,dh,new_H,H)
return new_match
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